Plug-in hybrid cars are far more fuel efficient than internal combustion engine cars, but it turns out that the way the cars' energy management systems balance the energy load between the battery and gas engine isn't always the most efficient.

Researchers at the University of California, Riverside looked to nature evolutionary systems for inspiration, particularly how birds flock and fly in formation in order to improve their energy efficiency, and came up with a new technology that improves that balance and reduces fuel consumption of plug-in hybrids by 30 percent.

The university says that while not all plug-in hybrids work the same, many of them have energy management systems (EMS) that first put the car in all-electric mode until the battery is drained and then switch into hybrid mode. Research has found that this split is not the most efficient way to balance the two power sources in the plug-in hybrid, but that blending the two power modes and using the battery throughout a car trip minimizes fuel consumption.

This way of managing the energy sources is more complicated though because it requires to car to balance the energy in the most efficient way depending on what the driving conditions and traffic are like throughout the route. This requires a lot of real-time information processing.

To achieve this, researchers Xuewei Qi, a postdoctoral researcher at the Center for Environmental Research and Technology (CE-CERT), and Matthew Barth, CE-CERT director and a professor of electrical and computer engineering at UCR, combined information coming from vehicle connectivity with cellular networks and crowdsourcing platforms with an evolutionary algorithm that mathematically illustrates energy saving behavior in nature like birds flocking and insect swarming.

"By mathematically modeling the energy saving processes that occur in nature, scientists have created algorithms that can be used to solve optimization problems in engineering," Qi said. "We combined this approach with connected vehicle technology to achieve energy savings of more than 30 percent. We achieved this by considering the charging opportunities during the trip--something that is not possible with existing EMS."

The efficiency of the vehicle can be optimized even further by having the system learn from what affected fuel consumption in the car's previous trips. Connected vehicles could then share the knowledge with other vehicles on the same network. This approach would mean that the EMS in each car was always improving and continually finding new ways to reduce fuel consumption and emissions.